Abstract: Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Abstract: Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Abstract: Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.